Python remains the dominant programming language for machine learning due to its comprehensive libraries and user-friendly nature. However, other languages like C++ and Rust are favored for performance-critical tasks due to their speed and optimization capabilities. Julia, while noted for its performance, is less widely adopted, and languages like Kotlin, Java, and C# are used for platform-specific ML applications. High-level languages such as Go, Swift, and Dart are chosen for their ability to compile to native code, enhancing performance, while R and SQL serve roles in statistical analysis and data management. CUDA is utilized for GPU programming to boost ML tasks, and JavaScript is often employed in full-stack web projects involving machine learning. Understanding the strengths of each language allows developers to choose the best tool for their specific ML needs.
